Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality

Abstract

Understanding the complex relationships between land use and stream water quality is critical for water pollution control and watershed management. This study aimed to investigate the relationship between land use types and water quality indicators at multiple spatial scales, namely, the watershed and riparian scales, using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR extended traditional regression models, such as OLS to address the spatial variations among variables. Our results indicated that the water quality indicators were significantly affected by agricultural and forested areas at both scales. We found that extensive agricultural land use had negative effects on water quality indicators, whereas, forested areas had positive effects on these indicators. The results also indicated that the watershed scale is effective for management and regulation of watershed land use, as the predictive power of the models is much greater at the watershed scale. The maps of estimated local parameters and local R2 in GWR models showcased the spatially varying relationships and indicated that the effects of land use on water quality varied over space. The results of this study reinforced the importance of watershed management in the planning, restoration, and management of stream water quality. It is also suggested that planners and managers may need to adopt different strategies, considering watershed characteristics—such as topographic features and meteorological conditions—and the source of pollutants, in managing stream water quality.

Probability maps of anthropogenic impacts affecting ecological status in European rivers

“…On the other hand, it was associated with an increased probability of occurrence of altered hydrology. This is counterintuitive given the multiple benefits associated to natural vegetation in riparian land ( Cole et al, 2020 , Park and Lee, 2020 ), and partly in contradiction with Schmidt et al (2019) , who found that natural land in floodplains was beneficial to river habitats. A possible explanation may lay in the resolution of spatial units and the ensuing range of shares of riparian land.…”

“…SI4k). Park and Lee (2020) observed that the effect of land use is not spatially stationary and may become detectable above some threshold fractions. Furthermore, the small size of CCM2 catchment may have also be the cause of errors when resampling abstractions and low flow alteration variables, originally provided in 5 × 5 km 2 grids ( Bisselink et al, 2018 ).…”

Eutrophication and the Ecological Health Risk

“…Park and Lee [ 24 ] investigated the relationship between land-use types and water quality indicators at two spatial scales (watershed and riparian) using ordinary least squares and geographically weighted regression models. Their results show water quality indicators to be significantly affected by agricultural and forested areas at both scales, with the watershed scale effective for the management and regulation of watershed land use.…”

“…Herein, I summarize the contributed papers in the context of eutrophication and related risks to the health of ecological systems to paint an overall picture of this Special Issue ( Table 1 ). Three studies focused on “eutrophication” [ 17 , 18 , 19 ], four on “ecological health risk” [ 20 , 21 , 22 , 23 ], and a further three papers on “ecosystem health and services” [ 24 , 25 , 26 ] have been included in this issue. A more detailed summary of all articles is offered in the following section.…”

Evaluating the Relationships between Riparian Land Cover Characteristics and Biological Integrity of Streams Using Random Forest Algorithms

“…Different statistical methods have been used to study the relationships between land cover characteristics and stream ecosystems, including multiple regression, regression trees, and redundancy analysis. Such conventional statistical methods assume the normality and spatial independence of the observed datasets, as well as linearity and non-multicollinearity between dependent and independent variables [ 20 , 21 ]. In most cases, however, these assumptions are difficult to satisfy in stream monitoring datasets, as land use types, landscape characteristics, water quality, and the biological communities of stream ecosystems are not spatially discrete or independent.…”